Multi-scale Aggregation Block. In order to extract rich features, we propose a Multi-scale Aggregation Block (MSAB).
MSAB is an efficient feature extraction module that can extract rich multi-scale features with few parameters, which is inspired by MSRB [1]} and MDCB [2]}, and the number of parameters of the module is reduced by using the channel splitting mechanism and dilated convolutional layers.
As shown in Figure REF , after a \(1 \times 1\)  convolutional layer, we use the channel splitting operation to divide the features into two groups.
Then, we apply the convolutional layer with different dilation rates on different groups to extract image features with different scales.
After that, all extracted features are concatenated, and the channel shuffle operation is applied to overcome the side effects brought by the channel splitting.
Finally, a \(1 \times 1\)  convolutional layer is used for feature fusion and the residual learning strategy is introduced to further improve the information flow.
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